Training Deep neural networks (DNNs) on noisy labeled datasets is a challenging problem, because learning on mislabeled examples deteriorates the performance of the network. As the ground truth availability is limited with real-world noisy datasets, previous papers created synthetic noisy datasets by randomly modifying the labels of training examples of clean datasets. However, no final conclusions can be derived by just using this random noise, since it excludes feature-dependent noise. Thus, it is imperative to generate feature-dependent noisy datasets that additionally provide ground truth. Therefore, we propose an intuitive approach to creating feature-dependent noisy datasets by utilizing the training predictions of DNNs on clean datasets that also retain true label information. We refer to these datasets as "Pseudo Noisy datasets". We conduct several experiments to establish that Pseudo noisy datasets resemble feature-dependent noisy datasets across different conditions. We further randomly generate synthetic noisy datasets with the same noise distribution as that of Pseudo noise (referred as "Randomized Noise") to empirically show that i) learning is easier with feature-dependent label noise compared to random noise, ii) irrespective of noise distribution, Pseudo noisy datasets mimic feature-dependent label noise and iii) current training methods are not generalizable to feature-dependent label noise. Therefore, we believe that Pseudo noisy datasets will be quite helpful to study and develop robust training methods.